Overview

Dataset statistics

Number of variables20
Number of observations1629
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory254.7 KiB
Average record size in memory160.1 B

Variable types

Numeric14
Categorical6

Alerts

three_g is highly correlated with four_gHigh correlation
four_g is highly correlated with three_gHigh correlation
px_width is highly correlated with px_heightHigh correlation
sc_h is highly correlated with sc_wHigh correlation
sc_w is highly correlated with sc_hHigh correlation
px_height is highly correlated with px_widthHigh correlation
df_index has unique values Unique
pc has 79 (4.8%) zeros Zeros
sc_w has 144 (8.8%) zeros Zeros

Reproduction

Analysis started2022-10-05 07:37:10.619166
Analysis finished2022-10-05 07:38:14.622318
Duration1 minute and 4 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1629
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1174.146102
Minimum1
Maximum2327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:14.909099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile120.8
Q1606
median1179
Q31756
95-th percentile2213.6
Maximum2327
Range2326
Interquartile range (IQR)1150

Descriptive statistics

Standard deviation670.6473106
Coefficient of variation (CV)0.5711787567
Kurtosis-1.192300847
Mean1174.146102
Median Absolute Deviation (MAD)575
Skewness-0.01205270568
Sum1912684
Variance449767.8153
MonotonicityNot monotonic
2022-10-05T02:38:15.250439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3931
 
0.1%
8511
 
0.1%
911
 
0.1%
7301
 
0.1%
23121
 
0.1%
9631
 
0.1%
9131
 
0.1%
19861
 
0.1%
14901
 
0.1%
14431
 
0.1%
Other values (1619)1619
99.4%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
51
0.1%
71
0.1%
81
0.1%
131
0.1%
151
0.1%
171
0.1%
191
0.1%
ValueCountFrequency (%)
23271
0.1%
23261
0.1%
23251
0.1%
23241
0.1%
23231
0.1%
23191
0.1%
23181
0.1%
23161
0.1%
23151
0.1%
23141
0.1%

talk_time
Real number (ℝ≥0)

Distinct19
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.98526703
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:15.907059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile19
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.433631643
Coefficient of variation (CV)0.4946289995
Kurtosis-1.208637624
Mean10.98526703
Median Absolute Deviation (MAD)5
Skewness0.01973719172
Sum17895
Variance29.52435283
MonotonicityNot monotonic
2022-10-05T02:38:16.219673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7107
 
6.6%
4103
 
6.3%
1599
 
6.1%
695
 
5.8%
1093
 
5.7%
1692
 
5.6%
1391
 
5.6%
1990
 
5.5%
1187
 
5.3%
1885
 
5.2%
Other values (9)687
42.2%
ValueCountFrequency (%)
280
4.9%
370
4.3%
4103
6.3%
576
4.7%
695
5.8%
7107
6.6%
884
5.2%
971
4.4%
1093
5.7%
1187
5.3%
ValueCountFrequency (%)
2081
5.0%
1990
5.5%
1885
5.2%
1772
4.4%
1692
5.6%
1599
6.1%
1478
4.8%
1391
5.6%
1275
4.6%
1187
5.3%

battery_power
Real number (ℝ≥0)

Distinct915
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1250.555556
Minimum502
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:16.611388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum502
5-th percentile574.8
Q1856
median1265
Q31615
95-th percentile1936.6
Maximum1998
Range1496
Interquartile range (IQR)759

Descriptive statistics

Standard deviation438.4124463
Coefficient of variation (CV)0.3505741463
Kurtosis-1.198661271
Mean1250.555556
Median Absolute Deviation (MAD)377
Skewness-0.02097780131
Sum2037155
Variance192205.4731
MonotonicityNot monotonic
2022-10-05T02:38:16.963625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19496
 
0.4%
13306
 
0.4%
17155
 
0.3%
7675
 
0.3%
11545
 
0.3%
9465
 
0.3%
7935
 
0.3%
5345
 
0.3%
6185
 
0.3%
12815
 
0.3%
Other values (905)1577
96.8%
ValueCountFrequency (%)
5022
0.1%
5033
0.2%
5044
0.2%
5062
0.1%
5073
0.2%
5082
0.1%
5091
 
0.1%
5103
0.2%
5114
0.2%
5121
 
0.1%
ValueCountFrequency (%)
19981
 
0.1%
19971
 
0.1%
19962
0.1%
19953
0.2%
19943
0.2%
19931
 
0.1%
19923
0.2%
19912
0.1%
19893
0.2%
19881
 
0.1%

pc
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.966236955
Minimum0
Maximum20
Zeros79
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:17.247737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.137808427
Coefficient of variation (CV)0.6158601741
Kurtosis-1.206497185
Mean9.966236955
Median Absolute Deviation (MAD)5
Skewness0.02244086549
Sum16235
Variance37.67269228
MonotonicityNot monotonic
2022-10-05T02:38:17.505457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1097
 
6.0%
2096
 
5.9%
192
 
5.6%
490
 
5.5%
989
 
5.5%
786
 
5.3%
1780
 
4.9%
079
 
4.8%
1578
 
4.8%
1376
 
4.7%
Other values (11)766
47.0%
ValueCountFrequency (%)
079
4.8%
192
5.6%
275
4.6%
371
4.4%
490
5.5%
559
3.6%
672
4.4%
786
5.3%
874
4.5%
989
5.5%
ValueCountFrequency (%)
2096
5.9%
1975
4.6%
1867
4.1%
1780
4.9%
1674
4.5%
1578
4.8%
1473
4.5%
1376
4.7%
1267
4.1%
1159
3.6%

three_g
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
1
1252 
0
377 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1629
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11252
76.9%
0377
 
23.1%

Length

2022-10-05T02:38:17.774259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T02:38:18.062555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
11252
76.9%
0377
 
23.1%

Most occurring characters

ValueCountFrequency (%)
11252
76.9%
0377
 
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1629
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11252
76.9%
0377
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
Common1629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11252
76.9%
0377
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11252
76.9%
0377
 
23.1%

mobile_wt
Real number (ℝ≥0)

Distinct121
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.0006139
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:18.298448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median140
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.48554919
Coefficient of variation (CV)0.2534670971
Kurtosis-1.245567705
Mean140.0006139
Median Absolute Deviation (MAD)31
Skewness0.0279042485
Sum228061
Variance1259.224201
MonotonicityNot monotonic
2022-10-05T02:38:18.619426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19926
 
1.6%
10525
 
1.5%
18224
 
1.5%
10124
 
1.5%
16921
 
1.3%
18520
 
1.2%
11120
 
1.2%
9319
 
1.2%
8819
 
1.2%
16018
 
1.1%
Other values (111)1413
86.7%
ValueCountFrequency (%)
8014
0.9%
8110
0.6%
8212
0.7%
8313
0.8%
8416
1.0%
8512
0.7%
8614
0.9%
8710
0.6%
8819
1.2%
8917
1.0%
ValueCountFrequency (%)
20017
1.0%
19926
1.6%
19817
1.0%
19717
1.0%
19615
0.9%
1955
 
0.3%
1945
 
0.3%
19312
0.7%
19215
0.9%
19116
1.0%

px_width
Real number (ℝ≥0)

HIGH CORRELATION

Distinct922
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1242.829957
Minimum500
Maximum1997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:18.948577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile574.4
Q1865
median1235
Q31615
95-th percentile1930.6
Maximum1997
Range1497
Interquartile range (IQR)750

Descriptive statistics

Standard deviation431.8262273
Coefficient of variation (CV)0.3474539899
Kurtosis-1.176062454
Mean1242.829957
Median Absolute Deviation (MAD)376
Skewness0.03944181928
Sum2024570
Variance186473.8906
MonotonicityNot monotonic
2022-10-05T02:38:19.254711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14298
 
0.5%
8747
 
0.4%
13456
 
0.4%
9526
 
0.4%
19426
 
0.4%
8316
 
0.4%
11996
 
0.4%
5895
 
0.3%
10135
 
0.3%
14635
 
0.3%
Other values (912)1569
96.3%
ValueCountFrequency (%)
5003
0.2%
5012
0.1%
5061
 
0.1%
5071
 
0.1%
5091
 
0.1%
5103
0.2%
5122
0.1%
5132
0.1%
5152
0.1%
5162
0.1%
ValueCountFrequency (%)
19971
 
0.1%
19961
 
0.1%
19953
0.2%
19941
 
0.1%
19921
 
0.1%
19893
0.2%
19884
0.2%
19871
 
0.1%
19861
 
0.1%
19851
 
0.1%

sc_h
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.38305709
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:19.519160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.233144954
Coefficient of variation (CV)0.3418497487
Kurtosis-1.211969187
Mean12.38305709
Median Absolute Deviation (MAD)4
Skewness-0.1021900341
Sum20172
Variance17.9195162
MonotonicityNot monotonic
2022-10-05T02:38:19.726889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17157
 
9.6%
7127
 
7.8%
12123
 
7.6%
18114
 
7.0%
16113
 
6.9%
11113
 
6.9%
15112
 
6.9%
14106
 
6.5%
13105
 
6.4%
19104
 
6.4%
Other values (5)455
27.9%
ValueCountFrequency (%)
570
4.3%
696
5.9%
7127
7.8%
891
5.6%
9104
6.4%
1094
5.8%
11113
6.9%
12123
7.6%
13105
6.4%
14106
6.5%
ValueCountFrequency (%)
19104
6.4%
18114
7.0%
17157
9.6%
16113
6.9%
15112
6.9%
14106
6.5%
13105
6.4%
12123
7.6%
11113
6.9%
1094
5.8%

sc_w
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.744014733
Minimum0
Maximum18
Zeros144
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:19.965779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.327377998
Coefficient of variation (CV)0.7533716745
Kurtosis-0.3736838351
Mean5.744014733
Median Absolute Deviation (MAD)3
Skewness0.6407417346
Sum9357
Variance18.72620033
MonotonicityNot monotonic
2022-10-05T02:38:20.187076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1175
10.7%
4156
9.6%
3154
9.5%
0144
8.8%
5138
8.5%
2124
 
7.6%
6113
 
6.9%
7106
 
6.5%
898
 
6.0%
1089
 
5.5%
Other values (9)332
20.4%
ValueCountFrequency (%)
0144
8.8%
1175
10.7%
2124
7.6%
3154
9.5%
4156
9.6%
5138
8.5%
6113
6.9%
7106
6.5%
898
6.0%
976
4.7%
ValueCountFrequency (%)
184
 
0.2%
1716
 
1.0%
1625
 
1.5%
1525
 
1.5%
1428
 
1.7%
1338
2.3%
1255
3.4%
1165
4.0%
1089
5.5%
976
4.7%

m_dep
Real number (ℝ≥0)

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4995702885
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:20.421759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.2896130809
Coefficient of variation (CV)0.5797243902
Kurtosis-1.285353051
Mean0.4995702885
Median Absolute Deviation (MAD)0.3
Skewness0.08524555276
Sum813.8
Variance0.08387573661
MonotonicityNot monotonic
2022-10-05T02:38:20.615572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1273
16.8%
0.2173
10.6%
0.7171
10.5%
0.8167
10.3%
0.5159
9.8%
0.9157
9.6%
0.3154
9.5%
0.6153
9.4%
0.4137
8.4%
185
 
5.2%
ValueCountFrequency (%)
0.1273
16.8%
0.2173
10.6%
0.3154
9.5%
0.4137
8.4%
0.5159
9.8%
0.6153
9.4%
0.7171
10.5%
0.8167
10.3%
0.9157
9.6%
185
 
5.2%
ValueCountFrequency (%)
185
 
5.2%
0.9157
9.6%
0.8167
10.3%
0.7171
10.5%
0.6153
9.4%
0.5159
9.8%
0.4137
8.4%
0.3154
9.5%
0.2173
10.6%
0.1273
16.8%

touch_screen
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
0
816 
1
813 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1629
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0816
50.1%
1813
49.9%

Length

2022-10-05T02:38:20.834835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T02:38:21.066988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0816
50.1%
1813
49.9%

Most occurring characters

ValueCountFrequency (%)
0816
50.1%
1813
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1629
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0816
50.1%
1813
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common1629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0816
50.1%
1813
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0816
50.1%
1813
49.9%

four_g
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
1
876 
0
753 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1629
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1876
53.8%
0753
46.2%

Length

2022-10-05T02:38:21.262799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T02:38:21.494536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1876
53.8%
0753
46.2%

Most occurring characters

ValueCountFrequency (%)
1876
53.8%
0753
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1629
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1876
53.8%
0753
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common1629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1876
53.8%
0753
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1876
53.8%
0753
46.2%

blue
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
0
853 
1
776 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1629
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0853
52.4%
1776
47.6%

Length

2022-10-05T02:38:21.691448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T02:38:21.923027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0853
52.4%
1776
47.6%

Most occurring characters

ValueCountFrequency (%)
0853
52.4%
1776
47.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1629
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0853
52.4%
1776
47.6%

Most occurring scripts

ValueCountFrequency (%)
Common1629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0853
52.4%
1776
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0853
52.4%
1776
47.6%

n_cores
Real number (ℝ≥0)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.524861878
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:22.099918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.29225851
Coefficient of variation (CV)0.5065919295
Kurtosis-1.242101759
Mean4.524861878
Median Absolute Deviation (MAD)2
Skewness-0.00969767195
Sum7371
Variance5.254449075
MonotonicityNot monotonic
2022-10-05T02:38:22.296782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7214
13.1%
3212
13.0%
4209
12.8%
8205
12.6%
6202
12.4%
1201
12.3%
2194
11.9%
5192
11.8%
ValueCountFrequency (%)
1201
12.3%
2194
11.9%
3212
13.0%
4209
12.8%
5192
11.8%
6202
12.4%
7214
13.1%
8205
12.6%
ValueCountFrequency (%)
8205
12.6%
7214
13.1%
6202
12.4%
5192
11.8%
4209
12.8%
3212
13.0%
2194
11.9%
1201
12.3%

wifi
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
1
828 
0
801 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1629
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1828
50.8%
0801
49.2%

Length

2022-10-05T02:38:22.536863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T02:38:22.770663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1828
50.8%
0801
49.2%

Most occurring characters

ValueCountFrequency (%)
1828
50.8%
0801
49.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1629
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1828
50.8%
0801
49.2%

Most occurring scripts

ValueCountFrequency (%)
Common1629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1828
50.8%
0801
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1828
50.8%
0801
49.2%

dual_sim
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.9 KiB
1
832 
0
797 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1629
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1832
51.1%
0797
48.9%

Length

2022-10-05T02:38:22.971498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T02:38:23.600055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1832
51.1%
0797
48.9%

Most occurring characters

ValueCountFrequency (%)
1832
51.1%
0797
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1629
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1832
51.1%
0797
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common1629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1832
51.1%
0797
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1832
51.1%
0797
48.9%

ram
Real number (ℝ≥0)

Distinct1217
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2120.357274
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:23.847502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile456.4
Q11211
median2137
Q33031
95-th percentile3834
Maximum3998
Range3742
Interquartile range (IQR)1820

Descriptive statistics

Standard deviation1079.789375
Coefficient of variation (CV)0.5092487894
Kurtosis-1.168537297
Mean2120.357274
Median Absolute Deviation (MAD)919
Skewness0.0311448587
Sum3454062
Variance1165945.095
MonotonicityNot monotonic
2022-10-05T02:38:24.158072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22276
 
0.4%
23346
 
0.4%
18515
 
0.3%
28224
 
0.2%
38344
 
0.2%
6904
 
0.2%
26104
 
0.2%
4614
 
0.2%
36154
 
0.2%
18864
 
0.2%
Other values (1207)1584
97.2%
ValueCountFrequency (%)
2561
0.1%
2582
0.1%
2591
0.1%
2621
0.1%
2651
0.1%
2731
0.1%
2771
0.1%
2782
0.1%
2821
0.1%
2851
0.1%
ValueCountFrequency (%)
39981
 
0.1%
39961
 
0.1%
39931
 
0.1%
39911
 
0.1%
39781
 
0.1%
39713
0.2%
39701
 
0.1%
39691
 
0.1%
39661
 
0.1%
39652
0.1%

int_memory
Real number (ℝ≥0)

Distinct63
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.60036832
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:24.516653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median33
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation17.99509966
Coefficient of variation (CV)0.5519906854
Kurtosis-1.196104348
Mean32.60036832
Median Absolute Deviation (MAD)16
Skewness0.02417941111
Sum53106
Variance323.8236117
MonotonicityNot monotonic
2022-10-05T02:38:24.807336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1645
 
2.8%
2739
 
2.4%
5738
 
2.3%
3334
 
2.1%
2333
 
2.0%
4233
 
2.0%
1032
 
2.0%
932
 
2.0%
731
 
1.9%
4431
 
1.9%
Other values (53)1281
78.6%
ValueCountFrequency (%)
229
1.8%
320
1.2%
412
 
0.7%
527
1.7%
629
1.8%
731
1.9%
829
1.8%
932
2.0%
1032
2.0%
1123
1.4%
ValueCountFrequency (%)
6429
1.8%
6323
1.4%
6218
1.1%
6125
1.5%
6022
1.4%
5910
 
0.6%
5826
1.6%
5738
2.3%
5626
1.6%
5526
1.6%

px_height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct926
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean641.1264579
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:25.124527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68
Q1286
median557
Q3936
95-th percentile1518.8
Maximum1960
Range1960
Interquartile range (IQR)650

Descriptive statistics

Standard deviation440.4137866
Coefficient of variation (CV)0.6869374694
Kurtosis-0.2080957794
Mean641.1264579
Median Absolute Deviation (MAD)311
Skewness0.6942521556
Sum1044395
Variance193964.3034
MonotonicityNot monotonic
2022-10-05T02:38:25.434169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3477
 
0.4%
2866
 
0.4%
4106
 
0.4%
2935
 
0.3%
3445
 
0.3%
3205
 
0.3%
7305
 
0.3%
6495
 
0.3%
5705
 
0.3%
1605
 
0.3%
Other values (916)1575
96.7%
ValueCountFrequency (%)
02
0.1%
11
 
0.1%
21
 
0.1%
32
0.1%
43
0.2%
61
 
0.1%
71
 
0.1%
82
0.1%
91
 
0.1%
101
 
0.1%
ValueCountFrequency (%)
19601
 
0.1%
19201
 
0.1%
19141
 
0.1%
19011
 
0.1%
18991
 
0.1%
18951
 
0.1%
18741
 
0.1%
18692
0.1%
18583
0.2%
18521
 
0.1%

clock_speed
Real number (ℝ≥0)

Distinct26
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.527501535
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.9 KiB
2022-10-05T02:38:25.727115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.3
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.8157418999
Coefficient of variation (CV)0.5340367138
Kurtosis-1.321472551
Mean1.527501535
Median Absolute Deviation (MAD)0.8
Skewness0.1741776272
Sum2488.3
Variance0.6654348472
MonotonicityNot monotonic
2022-10-05T02:38:25.980417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5335
20.6%
2.877
 
4.7%
2.165
 
4.0%
1.364
 
3.9%
2.364
 
3.9%
160
 
3.7%
2.456
 
3.4%
2.556
 
3.4%
1.656
 
3.4%
1.456
 
3.4%
Other values (16)740
45.4%
ValueCountFrequency (%)
0.5335
20.6%
0.656
 
3.4%
0.749
 
3.0%
0.844
 
2.7%
0.945
 
2.8%
160
 
3.7%
1.145
 
2.8%
1.243
 
2.6%
1.364
 
3.9%
1.456
 
3.4%
ValueCountFrequency (%)
324
 
1.5%
2.950
3.1%
2.877
4.7%
2.736
2.2%
2.645
2.8%
2.556
3.4%
2.456
3.4%
2.364
3.9%
2.247
2.9%
2.165
4.0%

Interactions

2022-10-05T02:38:09.795963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:17.580669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:21.607139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:25.474790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:29.742517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:33.851933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:38.176853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:41.990203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:45.733395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:50.119231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:53.655677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:57.833985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:01.662592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:05.427834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:10.022084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:18.131385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:21.847535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:25.725554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:29.984827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:34.180897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:38.425516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:42.215976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:45.979486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:50.331754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:53.920504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:58.084890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:01.891583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:05.671673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:10.293185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:18.499366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:22.127900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:26.011886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:30.249198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:34.431225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:38.685851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:42.486517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:46.295843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:50.568108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:54.225339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:58.359590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:02.181730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:06.335128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:10.562990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:18.745935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:22.444102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:26.311451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:30.540035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:34.691300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:38.993390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:42.769323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:46.562905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:50.813041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:54.496899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:58.614996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:02.474875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:06.623610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:10.819660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:19.026713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:22.765004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:26.641636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:30.800858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:34.989415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:39.284162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:43.084339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:47.209874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:51.069030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:54.769453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:58.887102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:02.763554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:06.993455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:11.054327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:19.272081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-05T02:37:26.922204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:31.063995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:35.255248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:39.551361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:43.391097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:47.480316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:51.348357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:55.045766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:59.154058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:03.028000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:07.300410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:11.305098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:19.556954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:23.313420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:27.201360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:31.379087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:35.505060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:39.836974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:43.675291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:47.769399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:51.643031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:55.316924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:59.422040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:03.299784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:07.568325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:11.563562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:19.795964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:23.581042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:27.821469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:31.656699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:35.855674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:40.090144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:43.916417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:48.052696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:51.873309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:55.594671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:59.724094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:03.572452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:07.843838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:11.825747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:20.051918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:23.857435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:28.120098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:32.034025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:36.145244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:40.350174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:44.163268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:48.328349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:52.122370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:55.866830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:59.984431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:03.853007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:08.131761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:12.061500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:20.291498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:24.107929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:28.378783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:32.298184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:36.393694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:40.592063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-05T02:37:48.592492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:52.363131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:56.111707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-05T02:38:04.100599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-05T02:38:12.312032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:20.542205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:24.378098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:28.631118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:32.589494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:36.655587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:40.867359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:44.634752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:48.887329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:52.628521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:56.807869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-05T02:38:08.708754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:12.584196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:20.826163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:24.640560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:28.916992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-05T02:37:36.942487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:41.149816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:44.904335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:49.242052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:52.878064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:57.051702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:00.824141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:04.642026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:09.002127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:12.834898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:21.060753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:24.918874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:29.202524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:33.229389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:37.588159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:41.420913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:45.175910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:49.611468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:53.129207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:57.296759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:01.098109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:04.894327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:09.257611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:13.090339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:21.359986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:25.206374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:29.480098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:33.511178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:37.889064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:41.712138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:45.467350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:49.878837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:53.412625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:37:57.568016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:01.394820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:05.176632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T02:38:09.544577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-05T02:38:26.284442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-05T02:38:26.822663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-05T02:38:27.372458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-05T02:38:27.833216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-05T02:38:28.231117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-05T02:38:13.569947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-05T02:38:14.296035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indextalk_timebattery_powerpcthree_gmobile_wtpx_widthsc_hsc_wm_deptouch_screenfour_gbluen_coreswifidual_simramint_memorypx_heightclock_speed
03934171520112018491620.61106011249151142.6
117281016781111345361900.9110300278644362.1
21909781708860717100.20012013862643681.3
314801514301601831182710.510011128322711320.5
4301895510115512541410.6101400197424280.5
51449915494191990730.7001411799621671.8
6211111846201597731560.7101110133319830.6
764391175911641394940.31017001944198731.3
817621614811319615331050.61115112945371160.7
9143489071201608961360.20013013911441442.6

Last rows

df_indextalk_timebattery_powerpcthree_gmobile_wtpx_widthsc_hsc_wm_deptouch_screenfour_gbluen_coreswifidual_simramint_memorypx_heightclock_speed
16191433151982161129187316140.11007103511247641.1
162016369600201127471800.80005001655143200.5
1621231514183513119358318170.1011700448113852.3
162235719765110881851610.310181022564413642.5
16231251618142114178618120.4010201355697561.4
16247941311221411207901120.6100410329123311.2
1625131359991116774517100.1011401134427201.8
16261983161501151187105613120.2110110248721981.1
16272248161456201193142712110.210030136244912851.6
162883715122511138110112110.6110701267522170.7